Open Access
Issue |
E3S Web Conf.
Volume 448, 2023
The 8th International Conference on Energy, Environment, Epidemiology and Information System (ICENIS 2023)
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Article Number | 02058 | |
Number of page(s) | 14 | |
Section | Information System | |
DOI | https://doi.org/10.1051/e3sconf/202344802058 | |
Published online | 17 November 2023 |
- E. A. P. Steegers, P. Von Dadelszen, J. J. Duvekot, and R. Pijnenborg, Pre-eclampsia, Lancet, 376, 9741, 631–644 (2010) [Google Scholar]
- M. W. L. Moreira, J. J. P. C. Rodrigues, A. M. B. Oliveira, R. F. Ramos, and K. Saleem, A preeclampsia diagnosis approach using Bayesian networks, 2016 IEEE Int. Conf. Commun. ICC 2016, (2016) [Google Scholar]
- F. M. Musyoka, M. M. Thiga, and G. M. Muketha, A 24-hour ambulatory blood pressure monitoring system for preeclampsia management in antenatal care, Informatics Med. Unlocked, 16, June (2019) [Google Scholar]
- M. L. Costa and J. G. Cecatti, Preeclampsia in 2018: Revisiting Concepts, Physiopathology, and Prediction, 2018 (2018) [Google Scholar]
- R. Nirupama, S. Divyashree, P. Janhavi, S. P. Muthukumar, and P. V Ravindra, ScienceDirect Preeclampsia: Pathophysiology and management, J. Gynecol. Obstet. Hum. Reprod., 50, 2, 101975 (2021) [CrossRef] [Google Scholar]
- L. C. Poon and K. H. Nicolaides, Early Prediction of Preeclampsia, Obstet. Gynecol. Int., 2014, 2, 1–11 (2014) [CrossRef] [Google Scholar]
- P. Von Dadelszen and L. A. Magee, Pre-eclampsia: An Update, Curr. Hypertens. Rep., 16, 8 (2014) [Google Scholar]
- H. Sufriyana, Y. W. Wu, and E. C. Y. Su, Artificial intelligence-assisted prediction of preeclampsia: Development and external validation of a nationwide health insurance dataset of the BPJS Kesehatan in Indonesia, EBioMedicine, 54 (2020) [Google Scholar]
- J. Zhang et al., Early prediction of preeclampsia and small-for-gestational-age via multi-marker model in Chinese pregnancies: A prospective screening study, BMC Pregnancy Childbirth, 19, 1, 1–10 (2019) [CrossRef] [PubMed] [Google Scholar]
- L. Myatt, Expert Review The prediction of preeclampsia: the way forward, Am. J. Obstet. Gynecol., (2020) [Google Scholar]
- A. C. De Kat, J. Hirst, M. Woodward, S. Kennedy, and S. A. Peters, Prediction models for preeclampsia: A systematic review, Pregnancy Hypertens., 16, 48–66, March (2019) [CrossRef] [Google Scholar]
- E. Purwanti and I. S. Preswari, Early Risk Detection of Pre-eclampsia for Pregnant women using Artificial Neural Network, 15, 2, 71–80 (2019) [Google Scholar]
- A. Martinez-velasco and L. Miralles, Machine Learning Approach for Pre-Eclampsia Risk Factors Association Machine Learning Approach for Pre-Eclampsia Risk Factors Association, January 2019 (2018) [Google Scholar]
- M. A. Zayyad and M. Toycan, Factors affecting sustainable adoption of e-health technology in developing countries: An exploratory survey of Nigerian hospitals from the perspective of healthcare professionals, PeerJ, 2018, 3 (2018) [Google Scholar]
- O. Oti, I. Azimi, A. Anzanpour, A. M. Rahmani, A. Axelin, and P. Liljeberg, Iot-based healthcare system for real-Time maternal stress monitoring, Proc. - 2018 IEEE/ACM Int. Conf. Connect. Heal. Appl. Syst. Eng. Technol. CHASE 2018, 57–62 (2019) [Google Scholar]
- J. H. Jhee et al., Prediction model development of late-onset preeclampsia using machine learning-based methods, 1–12 (2019) [Google Scholar]
- J. Allotey et al., Development and validation of prediction models for risk of adverse outcomes in women with early-onset pre-eclampsia: protocol of the prospective cohort PREP study, Diagnostic Progn. Res., 1, 1, 1–8 (2017) [CrossRef] [Google Scholar]
- S. Swayamsiddha and C. Mohanty, Application of cognitive Internet of Medical Things for COVID-19 pandemic, Diabetes Metab. Syndr. Clin. Res. Rev., 14, 5, 911–915 (2020) [CrossRef] [Google Scholar]
- S. Suryono, A. Khuriati, and T. Mantoro, A fuzzy rule-based fog – cloud computing for solar panel disturbance investigation, Cogent Eng., 6, 00, 1-19 (2019) [CrossRef] [Google Scholar]
- S. B. Baker, W. E. I. Xiang, S. Member, and I. A. N. Atkinson, Internet of Things for Smart Healthcare: Technologies, Challenges, and Opportunities, 5 (2017) [Google Scholar]
- D. V. Dimitrov, Medical internet of things and big data in healthcare, Healthc. Inform. Res., 22, 3, 156–163 (2016) [CrossRef] [PubMed] [Google Scholar]
- Y. J. Fan, Y. H. Yin, L. Da Xu, Y. Zeng, and F. Wu, IoT-based smart rehabilitation system, IEEE Trans. Ind. Informatics, 10, 2, 1568–1577 (2014) [CrossRef] [Google Scholar]
- S. M. R. Islam, D. Kwak, M. H. Kabir, M. Hossain, and K. S. Kwak, The internet of things for health care: A comprehensive survey, IEEE Access, 3, 678–708 (2015) [CrossRef] [Google Scholar]
- M. S. Mahdavinejad, M. Rezvan, M. Barekatain, P. Adibi, P. Barnaghi, and A. P. Sheth, Machine learning for internet of things data analysis: a survey, Digit. Commun. Networks, 4, 3, 161–175 (2018) [CrossRef] [Google Scholar]
- T. Thangamani, R. Prabha, M. Prasad, U. Kumari, R. Kv, and S. Abidin, IoT Defense Machine Learning: Emerging Solutions and Future Problems, Microprocess. Microsyst., 104043 (2021) [Google Scholar]
- A. Whitmore, A. Agarwal, and L. Da Xu, The Internet of Things — A survey of topics and trends, March 2014, 261–274 (2015) [Google Scholar]
- M. Talal et al., Smart Home-based IoT for Real-time and Secure Remote Health Monitoring of Triage and Priority System using Body Sensors: Multi-driven Systematic Review (2019) [Google Scholar]
- M. Talal and K. L. T. W. L. Shir, A survey on communication components for IoT- based technologies in smart homes, Telecommun. Syst. (2018) [Google Scholar]
- A. L. Review, IoT Wearable Sensors and Devices in Elderly Care: Cvd (2020) [Google Scholar]
- A. Rahaman, M. Islam, R. Islam, M. S. Sadi, and S. Nooruddin, Revue d’ Intelligence Artificielle Developing IoT Based Smart Health Monitoring Systems: A Review, 33, 6, 435–440 (2020) [Google Scholar]
- T. O. Takpor and A. A. Atayero, Integrating Internet of Things and EHealth Solutions for Students Healthcare, I, 1–4 (2015) [Google Scholar]
- S. A. Nurhafid and T. Afriyani, PENGGUNAAN MOBILE HEALTH DALAM USAHA MONITORING, 5, 1 (2017) [Google Scholar]
- B. Farahani, F. Firouzi, V. Chang, and M. Badaroglu, Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare, Futur. Gener. Comput. Syst., 78, 659–676 (2018) [Google Scholar]
- M. A. G. Santos, R. Munoz, R. Olivares, P. P. R. Filho, J. Del Ser, and V. H. C. de Albuquerque, Online heart monitoring systems on the internet of health things environments: A survey, a reference model and an outlook, Inf. Fusion, 53, December 2018, 222–239 (2020) [CrossRef] [Google Scholar]
- Z. Al-makhadmeh and A. Tolba, Utilizing IoT wearable medical device for heart disease prediction using higher order Boltzmann model: A classification approach, Measurement, 147, 106815 (2019) [CrossRef] [Google Scholar]
- K. Guan, M. Shao, and S. Wu, Research Article A Remote Health Monitoring System for the Elderly Based on Smart Home Gateway, 2017 (2017) [Google Scholar]
- O. Al Shorman, B. Al Shorman, M. Al-Khassaweneh, and F. Alkahtani, A review of internet of medical things (IoMT) - Based remote health monitoring through wearable sensors: A case study for diabetic patients, Indones. J. Electr. Eng. Comput. Sci., 20, 1, 414–422 (2020) [Google Scholar]
- P. Kumar and K. Silambarasan, Enhancing the Performance of Healthcare Service in IoT and Cloud Using Optimized Techniques, IETE J. Res., 0, 0, 1–10 (2019) [Google Scholar]
- Z. Ashfaq et al., A review of enabling technologies for Internet of Medical Things (IoMT) Ecosystem, Ain Shams Eng. J., 13, 4, 101660 (2022) [CrossRef] [Google Scholar]
- A. Gatouillat, Y. Badr, B. Massot, and E. Sejdic, Internet of Medical Things: A Review of Recent Contributions Dealing with Cyber-Physical Systems in Medicine, IEEE Internet Things J., 5, 5, 3810–3822 (2018) [CrossRef] [Google Scholar]
- G. J. Joyia, R. M. Liaqat, A. Farooq, and S. Rehman, Internet of medical things (IOMT): Applications, benefits and future challenges in healthcare domain, J. Commun., 12, 4, 240–247 (2017) [Google Scholar]
- S. Rani, S. H. Ahmed, R. Talwar, J. Malhotra, and H. Song, IoMT: A Reliable Cross Layer Protocol for Internet of Multimedia Things, 4662, c, 1–9 (2017) [Google Scholar]
- L. Haoyu, L. Jianxing, N. Arunkumar, A. F. Hussein, and M. M. Jaber, An IoMT cloud-based real time sleep apnea detection scheme by using the SpO2 estimation supported by heart rate variability, Futur. Gener. Comput. Syst., 98, 69–77 (2019) [CrossRef] [Google Scholar]
- Y. Jin, H. Yu, Y. Zhang, N. Pan, and M. Guizani, Predictive analysis in outpatients assisted by the Internet of Medical Things, Futur. Gener. Comput. Syst., 98, 219–226 (2019) [CrossRef] [Google Scholar]
- S. Sudevan and M. Joseph, Internet of things: Incorporation into healthcare monitoring, 2019 4th MEC Int. Conf. Big Data Smart City, ICBDSC 2019, 1–4 (2019) [Google Scholar]
- M. Cornacchia, K. Ozcan, Y. Zheng, and S. Velipasalar, A Survey on Activity Detection and Classification Using Wearable Sensors, IEEE Sens. J., 17, 2, 386–403 (2017) [CrossRef] [Google Scholar]
- S. Ketu and P. K. Mishra, Internet of Healthcare Things: A contemporary survey, J. Netw. Comput. Appl., 192, March, 103179 (2021) [Google Scholar]
- S. Ju, Y. Sun, and Y. Su, Internet of things smart medical system and nursing intervention of glucocorticoid drug use, Microprocess. Microsyst., 83, December 2020 (2021) [Google Scholar]
- Z. N. Aghdam, A. M. Rahmani, and M. Hosseinzadeh, The Role of the Internet of Things in Healthcare: Future Trends and Challenges, Comput. Methods Programs Biomed., 199, 105903 (2021) [CrossRef] [Google Scholar]
- C. Tian, X. Chen, D. Guo, J. Sun, L. Liu, and J. Hong, Analysis and design of security in Internet of things, Proc. - 2015 8th Int. Conf. Biomed. Eng. Informatics, BMEI 2015, 61373147, 678–684 (2016) [Google Scholar]
- J. Torrado et al., Preeclampsia Is Associated with Increased Central Aortic Pressure, Elastic Arteries Stiffness and Wave Reflections, and Resting and Recruitable Endothelial Dysfunction, Int. J. Hypertens., 2015 (2015) [CrossRef] [Google Scholar]
- J. Wan et al., Wearable IoT enabled real-time health monitoring system, Eurasip J. Wirel. Commun. Netw., 2018, 1 (2018) [CrossRef] [Google Scholar]
- Z. Baloch, F. K. Shaikh, and M. A. Unar, A context-aware data fusion approach for health-IoT, Int. J. Inf. Technol., 10, 3, 241–245 (2018) [Google Scholar]
- A. Botta, W. De Donato, V. Persico, and A. Pescapé, Integration of Cloud computing and Internet of Things: A survey, Futur. Gener. Comput. Syst., 56, 684–700 (2016) [CrossRef] [Google Scholar]
- V. Osmani, S. Balasubramaniam, and D. Botvich, Human activity recognition in pervasive health-care: Supporting efficient remote collaboration, J. Netw. Comput. Appl., 31, 4, 628–655 (2018) [Google Scholar]
- G. J. Joyia, R. M. Liaqat, A. Farooq, and S. Rehman, Internet of Medical Things (IOMT): Applications, Benefits and Future Challenges in Healthcare Domain, 12, 4 (2017) [Google Scholar]
- Q. Wu, S. Member, G. Ding, S. Member, Y. Xu, and S. Member, Cognitive Internet of Things: A New Paradigm beyond Connection, 1–15 [Google Scholar]
- B. Farahani et al., Towards Fog-driven IoT eHealth: Promises and Challenges of IoT in Medicine and Healthcare, Futur. Gener. Comput. Syst. (2017) [Google Scholar]
- X. Li, Y. Lu, X. Fu, and Y. Qi, Building the Internet of Things platform for smart maternal healthcare services with wearable devices and cloud computing, 118, 282–296 (2021) [Google Scholar]
- P. D. Singh, G. Dhiman, and R. Sharma, Internet of Things for sustaining a smart and secure healthcare system, Sustain. Comput. Informatics Syst., 33, September 2021, p. 100622 (2022) [CrossRef] [Google Scholar]
- T. Zhang et al., A Joint Deep Learning and Internet of Medical Things Driven Framework for Elderly Patients, 8 (2020) [Google Scholar]
- N. Jin, X. Zhang, Z. Hou, I. Sanz-prieto, and B. Sani, Aggression and Violent Behavior IoT based psychological and physical stress evaluation in sportsmen using heart rate variability, Aggress. Violent Behav., February, 101587 (2021) [Google Scholar]
- A. Iyda et al., A conceptual IoT-based early-warning architecture for remote monitoring of COVID-19 patients in wards and at home, Internet of Things, xxxx, 100399 (2021) [Google Scholar]
- D. Gupta, S. Bhatt, M. Gupta, and A. S. Tosun, Future smart connected communities to fight COVID-19 outbreak, arXiv, 13, 100342 (2020) [Google Scholar]
- S. Wu, R. Chiang, S. Chang, and W. Chang, An Interactive Telecare System Enhanced with IoT Technology, 62–69 (2017) [Google Scholar]
- K. Naseer, S. Din, G. Jeon, and F. Piccialli, An accurate and dynamic predictive model for a smart M-Health system using machine learning, Inf. Sci. (Ny)., 538, 486–502 (2020) [CrossRef] [Google Scholar]
- E. Hossain, S. Uddin, and A. Khan, Network analytics and machine learning for predictive risk modelling of cardiovascular disease in patients with type 2 diabetes, Expert Syst. Appl., 164, April 2020, p. 113918 (2021) [CrossRef] [Google Scholar]
- F. Qin, D. Wang, B. Hu, and C. Wu, Health status prediction for the elderly based on machine learning, 90, April (2020) [Google Scholar]
- Y. Bao, N. A. Medland, C. K. Fairley, and J. Wu, Predicting the diagnosis of HIV and sexually transmitted infections among men who have sex with men using machine learning approaches, J. Infect., xxxx (2020) [Google Scholar]
- I. Marić et al., Early prediction of preeclampsia via machine learning, Am. J. Obstet. Gynecol. MFM, 2, 2, 100100 (2020) [CrossRef] [Google Scholar]
- 2 and Jorge Londoño3 Macarena Espinilla, 1 Javier Medina, 1 Ángel-Luis García- Fernández, 1 Sixto Campaña, Fuzzy Intelligent System for Patients with Preeclampsia in Wearable Devices, Mob. Inf. Syst., 2017 (2017) [Google Scholar]
- I. R. Hardini, A Survey on Machine learning and IoT, 4, 99–113 (2019) [Google Scholar]
- S. D. Auger, B. M. Jacobs, R. Dobson, C. R. Marshall, and A. J. Noyce, Big data, machine learning and artificial intelligence: A neurologist’s guide, Pract. Neurol., 21, 1, 4–11 (2021) [Google Scholar]
- G. L. Stavrinides and H. D. Karatza, A hybrid approach to scheduling real-time IoT workflows in fog and cloud environments, Multimed. Tools Appl., 24639–24655 (2018) [Google Scholar]
- V. A. Siris, N. Fotiou, A. Mertzianis, and G. C. Polyzos, Smart application-aware IoT data collection, Journal of Reliable Intelligent Environments, 5, 1, 17–28 (2019) [CrossRef] [Google Scholar]
- I. Azimi, T. Pahikkala, A. M. Rahmani, H. Niela-Vilén, A. Axelin, and P. Liljeberg, Missing data resilient decision-making for healthcare IoT through personalization: A case study on maternal health, Futur. Gener. Comput. Syst., 96, 297–308 (2019) [CrossRef] [Google Scholar]
- M. U. Farooq, M. Waseem, S. Mazhar, A. Khairi, and T. Kamal, A Review on Internet of Things (IoT), Int. J. Comput. Appl., 113, 1, 1–7 (2015) [Google Scholar]
- A. Rani and S. Kumar, A survey of security in wireless sensor networks, 3rd IEEE Int. Conf (2017) [Google Scholar]
- H. STEFAN and L. A. Md, Cloud Computing Security Threats and Solutions, i- manager’s J. Cloud Comput., 4, 2, 1 (2017) [Google Scholar]
- R. Shobha, B. Prakash, and R. Ragiri, Security trends in Internet of Things: a survey, SN Appl. Sci., January (2021) [Google Scholar]
- O. Simbolon, Predicting the Risk of Preeclampsia using Soft Voting-based Ensemble and Its Recommendation (2020) [Google Scholar]
- M. Ahmed and M. A. Kashem, IoT Based Risk Level Prediction Model for Maternal Health Care in the Context of Bangladesh, 2020 2nd Int. Conf. Sustain. Technol. Ind. 4.0, STI 2020, 0, 19–20 (2020) [Google Scholar]
- S. S. Amala and S. Mythili, IoT Based Health Care Monitoring System for Rural Pregnant Women, Int. J. Adv. Res. Electron. Commun. Eng., 6, 11, 2278–909 (2017) [Google Scholar]
- J. A. L. Marques et al., IoT-Based Smart Health System for Ambulatory Maternal and Fetal Monitoring, IEEE Internet Things J., 8, 23, 16814–16824 (2021) [CrossRef] [Google Scholar]
- F. Sarhaddi et al., Long-term iot-based maternal monitoring: System design and evaluation, Sensors, 21, 7 (2021) [Google Scholar]
- M. R. Dhivya, A. Ananthalakshmi, T. R. Harini, and M. Lavanya, Monitoring and Shaping the Future of Pregnant Women in Rural Areas Using IoT, Irjmets.Com, 04, 2482–2488 (2021). Retrieved from http://www.irjmets.com/uploadedfiles/paper/volume3/issue_4_april_2021/9239/1628083375.pdf [Google Scholar]
- J. F. M. Van Den Heuvel, A. T. Lely, J. J. Huisman, J. C. A. Trappenburg, A. Franx, and M. N. Bekker, Safe @ Home: Digital health platform facilitating a new care path for women at increased risk of preeclampsia – A case-control study, Pregnancy Hypertens., 22, July, 30–36 (2020) [CrossRef] [Google Scholar]
- P. N., S. D., and A. G., Internet of Things (IOT)-Data Security Challenges and Solutions, Int. Res. J. Adv. Sci. Hub, 3, Special Issue 6S, 144–147 (2021) [Google Scholar]
- S. Veazie et al., Rapid Evidence Review of Mobile Applications for Self-management of Diabetes, J. Gen. Intern. Med., 33, 7, 1167–1176 (2018) [CrossRef] [PubMed] [Google Scholar]
- J. T. Kelly, K. L. Campbell, E. Gong, and P. Scuffham, The Internet of Things: Impact and Implications for Health Care Delivery, J. Med. Internet Res., 22, 11 (2020) [Google Scholar]
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